Is a small muscle mass index really detrimental for insulin sensitivity in postmenopausal women of various body composition status?
Bibliographic record
Abstract
OBJECTIVES: We sought to determine if a small muscle mass index (MMI) is actually detrimental for insulin sensitivity when studying a large group of postmenopausal women displaying various body composition statuses and when age and visceral fat mass (VFM) are taken into account. METHODS: A cross-sectional study was conducted in 99 healthy postmenopausal women with a BMI of 28±4 kg/m(2). Fat mass and total fat-free mass (FFM) were obtained from DXA and VFM and MMI were estimated respectively by the equation of Bertin and by: Total FFM (kg)/height (m)(2). Fasting plasma insulin and glucose were obtained to calculate QUICKI and HOMA as an insulin sensitivity index. RESULTS: Total MMI and VFM were both significantly inversely correlated with QUICKI and positively with HOMA even when adjusted for VFM. A stepwise linear regression confirmed Total MMI and VFM as independent predictors of HOMA and plasma insulin level. CONCLUSIONS: A small muscle mass might not be detrimental for the maintenance of insulin sensitivity and could even be beneficial in sedentary postmenopausal women. The impact of muscle mass loss on insulin sensitivity in older adults needs to be further investigated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".